Search tips
Search criteria 


Logo of amjepidLink to Publisher's site
Am J Epidemiol. 2013 July 1; 178(1): 154–155.
Published online 2013 June 21. doi:  10.1093/aje/kwt105
PMCID: PMC3698995

The Authors Reply

We thank Davey Smith et al. for their comments (1). Regarding the relationship between the inflammatory marker C-reactive protein and coronary heart disease (CHD), Davey Smith et al. provide a cautionary example of how observational data—no matter how large the study—may still not amount to proof of causality (1). Although several meta-analyses have shown a robust association between the two factors (25), Mendelian randomization studies using genetic instruments suggest that C-reactive protein may not be a cause of heart disease (6, 7). In contrast, another inflammatory marker, interleukin 6, may contribute to the risk of heart disease (8).

Mendelian randomization can potentially inform the process of developing new therapies (and point to associations that are likely to be noncausal) prior to proceeding to expensive phase III trials. In social epidemiology, however, identifying a genetic instrument with which to explore causality is difficult for most exposures. For example, we are unaware of any genetic variants that can be used as instruments for evaluating job strain. We have previously examined this exposure using nongenetic instruments, such as rates of hospital-ward bed occupancy for a study of job strain among nurses (9), but in general, finding a convincing instrument is hard and relies on the chance availability of natural experiments.

For the purpose of establishing cause and effect, randomized controlled trials (RCTs) remain the gold standard. Demonstrating a robust association between exposure and outcome through meta-analysis of observational data may inform the design of RCTs. First, meta-analysis provides an evaluation of the expected effect size informing decisions about the size of trials to be implemented. This is likely to be an upper- rather than a lower-bound effect, as many observational associations have been refuted or found to be inflated when tested in RCTs (10, 11). For job strain, for example, the standardized effect size for CHD risk based on individual-participant meta-analysis of published and unpublished observational data was only one-seventh that for lifestyle factors such as smoking, physical inactivity, and obesity (12, 13). This suggests that very large RCTs are needed to confirm or refute a causal job strain-CHD association (Table 1). Second, meta-analytical information on expected effect size may facilitate the evaluation of more fundamental questions, such as whether the logistical challenges and financial requirements of large-scale RCTs are justified. Job strain has been examined in relation to employees' mental well-being (14). In the light of current evidence (Table 1), adding randomization and sensitive surrogate markers of cardiovascular risk to such interventions might be a more feasible next step than a large-scale RCT with CHD incidence as the primary outcome.

Table 1.
Current Evidence on the Association Between Job Strain and Coronary Heart Disease


Dr. Kivimäki was supported by an Economic and Social Research Council professorial fellowship, the Medical Research Council of the United Kingdom (grant K013351), and the National Heart, Lung, and Blood Institute (grant R01 HL036310).

Conflict of interest: none declared.


1. Davey Smith G, Egger M, Ebrahim S. Re: “Need for more individual-level meta-analyses in social epidemiology: example of job strain and coronary heart disease” [letter]. Am J Epidemiol. 2013;178(1):153–154. [PubMed]
2. Danesh J, Collins R, Appleby P, et al. Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies. JAMA. 1998;279(18):1477–1482. [PubMed]
3. Danesh J, Whincup P, Walker M, et al. Low grade inflammation and coronary heart disease: prospective study and updated meta-analyses. BMJ. 2000;321(7255):199–204. [PMC free article] [PubMed]
4. Ridker PM, Hennekens CH, Buring JE, et al. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 2000;342(12):836–843. [PubMed]
5. Kaptoge S, Di Angelantonio E, Pennells L, et al. C-reactive protein, fibrinogen, and cardiovascular disease prediction. N Engl J Med. 2012;367(14):1310–1320. [PMC free article] [PubMed]
6. Wensley F, Gao P, Burgess S, et al. C Reactive Protein Coronary Heart Disease Genetics Collaboration (CCGC) Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data. BMJ. 2011;342:d548. [PMC free article] [PubMed]
 7. Lawlor DA, Harbord RM, Timpson NJ, et al. The association of C-reactive protein and CRP genotype with coronary heart disease: findings from five studies with 4,610 cases amongst 18,637 participants. PLoS ONE. 2008;3(8):e3011. [PMC free article] [PubMed]
 8. Hingorani AD, Casas JP. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. terleukin-6 Receptor Mendelian Randomisation Analysis (IL6R MR) Consortium. Lancet. 2012;379(9822):1214–1224. [PMC free article] [PubMed]
 9. Kivimäki M, Vahtera J, Kawachi I, et al. Psychosocial work environment as a risk factor for absence with a psychiatric diagnosis: an instrumental-variables analysis. Am J Epidemiol. 2010;172(2):167–172. [PMC free article] [PubMed]
10. Ioannidis JP, Haidich AB, Pappa M, et al. Comparison of evidence of treatment effects in randomized and nonrandomized studies. JAMA. 2001;286(7):821–830. [PubMed]
11. Ioannidis JP. Contradicted and initially stronger effects in highly cited clinical research. JAMA. 2005;294(2):218–228. [PubMed]
12. Kivimäki M, Nyberg ST, Batty GD, et al. Job strain as a risk factor for coronary heart disease: a collaborative meta-analysis of individual participant data. Lancet. 2012;380(9852):1491–1497. [PMC free article] [PubMed]
13. Kivimäki M, Nyberg ST, Fransson EI, et al. Associations of job strain and lifestyle risk factors with risk of coronary artery disease: a meta-analysis of individual participant data [published online ahead of print May 16, 2013] CMAJ. ( doi:10.1503/cmaj.121735). [PMC free article] [PubMed]
14. Brisson C, Cantin V, Larocque B, et al. Intervention research on work organization factors and health: research design and preliminary results on mental health. Can J Commun Ment Health. 2006;25(2):241–259.

Articles from American Journal of Epidemiology are provided here courtesy of Oxford University Press